Overview

Brought to you by YData

Dataset statistics

Number of variables 23
Number of observations 3677
Missing cells 6710
Missing cells (%) 7.9%
Duplicate rows 0
Duplicate rows (%) 0.0%
Total size in memory 2.2 MiB
Average record size in memory 632.0 B

Variable types

Categorical 10
Text 3
Numeric 10

Alerts

Property_type is highly overall correlated with bedRoom and 2 other fields High correlation
area is highly overall correlated with bathroom and 5 other fields High correlation
bathroom is highly overall correlated with area and 5 other fields High correlation
bedRoom is highly overall correlated with Property_type and 5 other fields High correlation
built_up_area is highly overall correlated with area and 4 other fields High correlation
carpet_area is highly overall correlated with area and 5 other fields High correlation
facing is highly overall correlated with built_up_area High correlation
price is highly overall correlated with Property_type and 7 other fields High correlation
price_per_sqft is highly overall correlated with price High correlation
servant room is highly overall correlated with bathroom and 1 other fields High correlation
super_built_up_area is highly overall correlated with Property_type and 7 other fields High correlation
store room is highly imbalanced (55.7%) Imbalance
facing has 1045 (28.4%) missing values Missing
super_built_up_area has 1802 (49.0%) missing values Missing
built_up_area has 1987 (54.0%) missing values Missing
carpet_area has 1805 (49.1%) missing values Missing
area is highly skewed (γ1 = 29.73095613) Skewed
built_up_area is highly skewed (γ1 = 40.70657243) Skewed
carpet_area is highly skewed (γ1 = 24.33323909) Skewed
floorNum has 129 (3.5%) zeros Zeros
luxury_score has 462 (12.6%) zeros Zeros

Reproduction

Analysis started 2025-06-26 05:18:42.549606
Analysis finished 2025-06-26 05:18:59.126227
Duration 16.58 seconds
Software version ydata-profiling vv4.16.1
Download configuration config.json

Variables

Property_type
Categorical

High correlation 

Distinct 2
Distinct (%) 0.1%
Missing 0
Missing (%) 0.0%
Memory size 248.6 KiB
flat
2818 
house
859 

Length

Max length 5
Median length 4
Mean length 4.2336144
Min length 4

Characters and Unicode

Total characters 15567
Distinct characters 9
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row house
2nd row flat
3rd row flat
4th row house
5th row house

Common Values

Value Count Frequency (%)
flat 2818
76.6%
house 859
 
23.4%

Length

2025-06-26T05:18:59.213432 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-26T05:18:59.312796 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
flat 2818
76.6%
house 859
 
23.4%

Most occurring characters

Value Count Frequency (%)
f 2818
18.1%
l 2818
18.1%
a 2818
18.1%
t 2818
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 15567
100.0%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
f 2818
18.1%
l 2818
18.1%
a 2818
18.1%
t 2818
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%

Most occurring scripts

Value Count Frequency (%)
Latin 15567
100.0%

Most frequent character per script

Latin
Value Count Frequency (%)
f 2818
18.1%
l 2818
18.1%
a 2818
18.1%
t 2818
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%

Most occurring blocks

Value Count Frequency (%)
ASCII 15567
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
f 2818
18.1%
l 2818
18.1%
a 2818
18.1%
t 2818
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%

society
Text

Distinct 676
Distinct (%) 18.4%
Missing 1
Missing (%) < 0.1%
Memory size 293.9 KiB
2025-06-26T05:18:59.637126 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Length

Max length 49
Median length 39
Mean length 16.869695
Min length 1

Characters and Unicode

Total characters 62013
Distinct characters 41
Distinct categories 5 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 308 ?
Unique (%) 8.4%

Sample

1st row independent
2nd row umang winter hills
3rd row ambience creacions
4th row dlf city plots phase 2
5th row suncity essel towers
Value Count Frequency (%)
independent 491
 
5.1%
the 350
 
3.6%
dlf 220
 
2.3%
park 209
 
2.2%
city 166
 
1.7%
emaar 155
 
1.6%
global 153
 
1.6%
m3m 152
 
1.6%
signature 150
 
1.6%
heights 134
 
1.4%
Other values (783) 7497
77.5%
2025-06-26T05:19:00.121947 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

Value Count Frequency (%)
e 6710
 
10.8%
6003
 
9.7%
a 5861
 
9.5%
r 4171
 
6.7%
n 4163
 
6.7%
i 3830
 
6.2%
t 3719
 
6.0%
s 3472
 
5.6%
l 2943
 
4.7%
o 2755
 
4.4%
Other values (31) 18386
29.6%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 55465
89.4%
Space Separator 6003
 
9.7%
Decimal Number 527
 
0.8%
Other Punctuation 10
 
< 0.1%
Dash Punctuation 8
 
< 0.1%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 6710
12.1%
a 5861
 
10.6%
r 4171
 
7.5%
n 4163
 
7.5%
i 3830
 
6.9%
t 3719
 
6.7%
s 3472
 
6.3%
l 2943
 
5.3%
o 2755
 
5.0%
d 2488
 
4.5%
Other values (16) 15353
27.7%
Decimal Number
Value Count Frequency (%)
3 207
39.3%
2 82
 
15.6%
1 75
 
14.2%
6 56
 
10.6%
8 32
 
6.1%
4 19
 
3.6%
5 17
 
3.2%
0 13
 
2.5%
9 13
 
2.5%
7 13
 
2.5%
Other Punctuation
Value Count Frequency (%)
, 7
70.0%
/ 2
 
20.0%
. 1
 
10.0%
Space Separator
Value Count Frequency (%)
6003
100.0%
Dash Punctuation
Value Count Frequency (%)
- 8
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 55465
89.4%
Common 6548
 
10.6%

Most frequent character per script

Latin
Value Count Frequency (%)
e 6710
12.1%
a 5861
 
10.6%
r 4171
 
7.5%
n 4163
 
7.5%
i 3830
 
6.9%
t 3719
 
6.7%
s 3472
 
6.3%
l 2943
 
5.3%
o 2755
 
5.0%
d 2488
 
4.5%
Other values (16) 15353
27.7%
Common
Value Count Frequency (%)
6003
91.7%
3 207
 
3.2%
2 82
 
1.3%
1 75
 
1.1%
6 56
 
0.9%
8 32
 
0.5%
4 19
 
0.3%
5 17
 
0.3%
0 13
 
0.2%
9 13
 
0.2%
Other values (5) 31
 
0.5%

Most occurring blocks

Value Count Frequency (%)
ASCII 62013
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 6710
 
10.8%
6003
 
9.7%
a 5861
 
9.5%
r 4171
 
6.7%
n 4163
 
6.7%
i 3830
 
6.2%
t 3719
 
6.0%
s 3472
 
5.6%
l 2943
 
4.7%
o 2755
 
4.4%
Other values (31) 18386
29.6%

sector
Text

Distinct 113
Distinct (%) 3.1%
Missing 0
Missing (%) 0.0%
Memory size 266.9 KiB
2025-06-26T05:19:00.430118 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Length

Max length 26
Median length 9
Mean length 9.3209138
Min length 7

Characters and Unicode

Total characters 34273
Distinct characters 31
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 1 ?
Unique (%) < 0.1%

Sample

1st row sector 12
2nd row sector 77
3rd row sector 22
4th row sector 25
5th row sector 28
Value Count Frequency (%)
sector 3452
46.8%
road 178
 
2.4%
sohna 166
 
2.2%
85 108
 
1.5%
102 107
 
1.4%
92 100
 
1.4%
69 93
 
1.3%
90 89
 
1.2%
65 87
 
1.2%
81 87
 
1.2%
Other values (106) 2915
39.5%
2025-06-26T05:19:00.823668 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

Value Count Frequency (%)
o 3807
11.1%
3705
10.8%
s 3697
10.8%
r 3697
10.8%
e 3542
10.3%
c 3503
10.2%
t 3463
10.1%
1 1076
 
3.1%
0 804
 
2.3%
8 780
 
2.3%
Other values (21) 6199
18.1%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 23299
68.0%
Decimal Number 7269
 
21.2%
Space Separator 3705
 
10.8%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
o 3807
16.3%
s 3697
15.9%
r 3697
15.9%
e 3542
15.2%
c 3503
15.0%
t 3463
14.9%
a 699
 
3.0%
d 249
 
1.1%
n 221
 
0.9%
h 203
 
0.9%
Other values (10) 218
 
0.9%
Decimal Number
Value Count Frequency (%)
1 1076
14.8%
0 804
11.1%
8 780
10.7%
9 764
10.5%
6 742
10.2%
7 684
9.4%
2 676
9.3%
3 666
9.2%
5 593
8.2%
4 484
6.7%
Space Separator
Value Count Frequency (%)
3705
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 23299
68.0%
Common 10974
32.0%

Most frequent character per script

Latin
Value Count Frequency (%)
o 3807
16.3%
s 3697
15.9%
r 3697
15.9%
e 3542
15.2%
c 3503
15.0%
t 3463
14.9%
a 699
 
3.0%
d 249
 
1.1%
n 221
 
0.9%
h 203
 
0.9%
Other values (10) 218
 
0.9%
Common
Value Count Frequency (%)
3705
33.8%
1 1076
 
9.8%
0 804
 
7.3%
8 780
 
7.1%
9 764
 
7.0%
6 742
 
6.8%
7 684
 
6.2%
2 676
 
6.2%
3 666
 
6.1%
5 593
 
5.4%

Most occurring blocks

Value Count Frequency (%)
ASCII 34273
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
o 3807
11.1%
3705
10.8%
s 3697
10.8%
r 3697
10.8%
e 3542
10.3%
c 3503
10.2%
t 3463
10.1%
1 1076
 
3.1%
0 804
 
2.3%
8 780
 
2.3%
Other values (21) 6199
18.1%

price
Real number (ℝ)

High correlation 

Distinct 473
Distinct (%) 12.9%
Missing 17
Missing (%) 0.5%
Infinite 0
Infinite (%) 0.0%
Mean 2.5336639
Minimum 0.07
Maximum 31.5
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 57.5 KiB
2025-06-26T05:19:00.955144 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum 0.07
5-th percentile 0.37
Q1 0.95
median 1.52
Q3 2.75
95-th percentile 8.5
Maximum 31.5
Range 31.43
Interquartile range (IQR) 1.8

Descriptive statistics

Standard deviation 2.9806235
Coefficient of variation (CV) 1.1764084
Kurtosis 14.933373
Mean 2.5336639
Median Absolute Deviation (MAD) 0.72
Skewness 3.2791705
Sum 9273.21
Variance 8.8841164
Monotonicity Not monotonic
2025-06-26T05:19:01.089677 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
1.25 80
 
2.2%
1.2 64
 
1.7%
1.5 64
 
1.7%
0.9 63
 
1.7%
1.1 62
 
1.7%
1.4 60
 
1.6%
1.3 57
 
1.6%
0.95 52
 
1.4%
2 52
 
1.4%
1.6 48
 
1.3%
Other values (463) 3058
83.2%
Value Count Frequency (%)
0.07 1
 
< 0.1%
0.16 1
 
< 0.1%
0.17 1
 
< 0.1%
0.19 1
 
< 0.1%
0.2 8
0.2%
0.21 6
0.2%
0.22 8
0.2%
0.23 1
 
< 0.1%
0.24 6
0.2%
0.25 11
0.3%
Value Count Frequency (%)
31.5 1
 
< 0.1%
27.5 1
 
< 0.1%
26 2
0.1%
25 1
 
< 0.1%
24 1
 
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
20 3
0.1%
19.5 2
0.1%
19 3
0.1%

price_per_sqft
Real number (ℝ)

High correlation 

Distinct 2651
Distinct (%) 72.4%
Missing 17
Missing (%) 0.5%
Infinite 0
Infinite (%) 0.0%
Mean 13892.668
Minimum 4
Maximum 600000
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 57.5 KiB
2025-06-26T05:19:01.233415 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum 4
5-th percentile 4715.95
Q1 6817.25
median 9020
Q3 13880.5
95-th percentile 33333
Maximum 600000
Range 599996
Interquartile range (IQR) 7063.25

Descriptive statistics

Standard deviation 23210.067
Coefficient of variation (CV) 1.6706702
Kurtosis 186.92801
Mean 13892.668
Median Absolute Deviation (MAD) 2794
Skewness 11.43719
Sum 50847166
Variance 5.3870722 × 108
Monotonicity Not monotonic
2025-06-26T05:19:01.401694 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
10000 27
 
0.7%
8000 19
 
0.5%
5000 17
 
0.5%
12500 14
 
0.4%
11111 13
 
0.4%
6666 13
 
0.4%
22222 13
 
0.4%
7500 12
 
0.3%
8333 12
 
0.3%
33333 11
 
0.3%
Other values (2641) 3509
95.4%
(Missing) 17
 
0.5%
Value Count Frequency (%)
4 1
< 0.1%
5 1
< 0.1%
7 1
< 0.1%
9 1
< 0.1%
53 1
< 0.1%
57 1
< 0.1%
58 2
0.1%
60 1
< 0.1%
61 1
< 0.1%
79 1
< 0.1%
Value Count Frequency (%)
600000 1
< 0.1%
400000 1
< 0.1%
315789 1
< 0.1%
308333 1
< 0.1%
290948 1
< 0.1%
283333 1
< 0.1%
266666 1
< 0.1%
261194 1
< 0.1%
245398 1
< 0.1%
241666 1
< 0.1%

area
Real number (ℝ)

High correlation  Skewed 

Distinct 1312
Distinct (%) 35.8%
Missing 17
Missing (%) 0.5%
Infinite 0
Infinite (%) 0.0%
Mean 2888.3311
Minimum 50
Maximum 875000
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 57.5 KiB
2025-06-26T05:19:01.548270 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum 50
5-th percentile 518.85
Q1 1232.25
median 1733
Q3 2300
95-th percentile 4246.2
Maximum 875000
Range 874950
Interquartile range (IQR) 1067.75

Descriptive statistics

Standard deviation 23167.506
Coefficient of variation (CV) 8.0210699
Kurtosis 942.02903
Mean 2888.3311
Median Absolute Deviation (MAD) 533
Skewness 29.730956
Sum 10571292
Variance 5.3673333 × 108
Monotonicity Not monotonic
2025-06-26T05:19:01.694614 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
1650 54
 
1.5%
1350 48
 
1.3%
1800 47
 
1.3%
1950 43
 
1.2%
3240 43
 
1.2%
2700 39
 
1.1%
900 38
 
1.0%
2000 33
 
0.9%
2250 25
 
0.7%
2400 23
 
0.6%
Other values (1302) 3267
88.8%
Value Count Frequency (%)
50 4
0.1%
55 1
 
< 0.1%
56 1
 
< 0.1%
57 1
 
< 0.1%
60 2
0.1%
61 1
 
< 0.1%
67 2
0.1%
70 1
 
< 0.1%
72 1
 
< 0.1%
76 1
 
< 0.1%
Value Count Frequency (%)
875000 1
< 0.1%
642857 1
< 0.1%
620000 1
< 0.1%
566667 1
< 0.1%
215517 1
< 0.1%
98978 1
< 0.1%
82781 1
< 0.1%
65517 2
0.1%
65261 1
< 0.1%
58228 1
< 0.1%
Distinct 2355
Distinct (%) 64.0%
Missing 0
Missing (%) 0.0%
Memory size 428.2 KiB
2025-06-26T05:19:02.067800 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Length

Max length 124
Median length 119
Mean length 54.236062
Min length 12

Characters and Unicode

Total characters 199426
Distinct characters 35
Distinct categories 7 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 1849 ?
Unique (%) 50.3%

Sample

1st row Plot area 2700(250.84 sq.m.)
2nd row Super Built up area 1342(124.68 sq.m.)
3rd row Super Built up area 1860(172.8 sq.m.)Built Up area: 1600 sq.ft. (148.64 sq.m.)Carpet area: 1400 sq.ft. (130.06 sq.m.)
4th row Plot area 250(23.23 sq.m.)
5th row Plot area 5000(464.52 sq.m.)
Value Count Frequency (%)
area 5573
18.5%
sq.m 3655
12.1%
up 3020
 
10.0%
built 2316
 
7.7%
super 1875
 
6.2%
sq.ft 1751
 
5.8%
sq.m.)carpet 1185
 
3.9%
sq.m.)built 702
 
2.3%
carpet 683
 
2.3%
plot 681
 
2.3%
Other values (2846) 8700
28.9%
2025-06-26T05:19:02.632699 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

Value Count Frequency (%)
26464
 
13.3%
. 20389
 
10.2%
a 13154
 
6.6%
r 9456
 
4.7%
e 9320
 
4.7%
1 9205
 
4.6%
s 7567
 
3.8%
q 7431
 
3.7%
t 7324
 
3.7%
u 6770
 
3.4%
Other values (25) 82346
41.3%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 82758
41.5%
Decimal Number 47135
23.6%
Space Separator 26464
 
13.3%
Other Punctuation 23406
 
11.7%
Uppercase Letter 8593
 
4.3%
Open Punctuation 5535
 
2.8%
Close Punctuation 5535
 
2.8%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
a 13154
15.9%
r 9456
11.4%
e 9320
11.3%
s 7567
9.1%
q 7431
9.0%
t 7324
8.8%
u 6770
8.2%
p 6767
8.2%
m 5544
6.7%
l 3701
 
4.5%
Other values (5) 5724
6.9%
Decimal Number
Value Count Frequency (%)
1 9205
19.5%
0 6628
14.1%
2 5688
12.1%
5 4714
10.0%
3 3960
8.4%
4 3711
7.9%
6 3674
 
7.8%
7 3254
 
6.9%
8 3157
 
6.7%
9 3144
 
6.7%
Uppercase Letter
Value Count Frequency (%)
B 3020
35.1%
S 1875
21.8%
C 1872
21.8%
U 1145
 
13.3%
P 681
 
7.9%
Other Punctuation
Value Count Frequency (%)
. 20389
87.1%
: 3017
 
12.9%
Space Separator
Value Count Frequency (%)
26464
100.0%
Open Punctuation
Value Count Frequency (%)
( 5535
100.0%
Close Punctuation
Value Count Frequency (%)
) 5535
100.0%

Most occurring scripts

Value Count Frequency (%)
Common 108075
54.2%
Latin 91351
45.8%

Most frequent character per script

Latin
Value Count Frequency (%)
a 13154
14.4%
r 9456
10.4%
e 9320
10.2%
s 7567
8.3%
q 7431
8.1%
t 7324
8.0%
u 6770
7.4%
p 6767
7.4%
m 5544
 
6.1%
l 3701
 
4.1%
Other values (10) 14317
15.7%
Common
Value Count Frequency (%)
26464
24.5%
. 20389
18.9%
1 9205
 
8.5%
0 6628
 
6.1%
2 5688
 
5.3%
( 5535
 
5.1%
) 5535
 
5.1%
5 4714
 
4.4%
3 3960
 
3.7%
4 3711
 
3.4%
Other values (5) 16246
15.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 199426
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
26464
 
13.3%
. 20389
 
10.2%
a 13154
 
6.6%
r 9456
 
4.7%
e 9320
 
4.7%
1 9205
 
4.6%
s 7567
 
3.8%
q 7431
 
3.7%
t 7324
 
3.7%
u 6770
 
3.4%
Other values (25) 82346
41.3%

bedRoom
Real number (ℝ)

High correlation 

Distinct 19
Distinct (%) 0.5%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 3.3600761
Minimum 1
Maximum 21
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 57.5 KiB
2025-06-26T05:19:02.737263 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum 1
5-th percentile 2
Q1 2
median 3
Q3 4
95-th percentile 6
Maximum 21
Range 20
Interquartile range (IQR) 2

Descriptive statistics

Standard deviation 1.8976289
Coefficient of variation (CV) 0.56475771
Kurtosis 18.212873
Mean 3.3600761
Median Absolute Deviation (MAD) 1
Skewness 3.4851418
Sum 12355
Variance 3.6009954
Monotonicity Not monotonic
2025-06-26T05:19:03.195875 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
Value Count Frequency (%)
3 1496
40.7%
2 942
25.6%
4 660
17.9%
5 210
 
5.7%
1 124
 
3.4%
6 74
 
2.0%
9 41
 
1.1%
8 30
 
0.8%
7 28
 
0.8%
12 28
 
0.8%
Other values (9) 44
 
1.2%
Value Count Frequency (%)
1 124
 
3.4%
2 942
25.6%
3 1496
40.7%
4 660
17.9%
5 210
 
5.7%
6 74
 
2.0%
7 28
 
0.8%
8 30
 
0.8%
9 41
 
1.1%
10 20
 
0.5%
Value Count Frequency (%)
21 1
 
< 0.1%
20 1
 
< 0.1%
19 2
 
0.1%
18 2
 
0.1%
16 12
0.3%
14 1
 
< 0.1%
13 4
 
0.1%
12 28
0.8%
11 1
 
< 0.1%
10 20
0.5%

bathroom
Real number (ℝ)

High correlation 

Distinct 19
Distinct (%) 0.5%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 3.4245309
Minimum 1
Maximum 21
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 57.5 KiB
2025-06-26T05:19:03.322744 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum 1
5-th percentile 2
Q1 2
median 3
Q3 4
95-th percentile 6
Maximum 21
Range 20
Interquartile range (IQR) 2

Descriptive statistics

Standard deviation 1.9480681
Coefficient of variation (CV) 0.56885693
Kurtosis 17.542297
Mean 3.4245309
Median Absolute Deviation (MAD) 1
Skewness 3.2488298
Sum 12592
Variance 3.7949693
Monotonicity Not monotonic
2025-06-26T05:19:03.429789 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
Value Count Frequency (%)
3 1077
29.3%
2 1047
28.5%
4 820
22.3%
5 294
 
8.0%
1 156
 
4.2%
6 117
 
3.2%
9 41
 
1.1%
7 40
 
1.1%
8 25
 
0.7%
12 22
 
0.6%
Other values (9) 38
 
1.0%
Value Count Frequency (%)
1 156
 
4.2%
2 1047
28.5%
3 1077
29.3%
4 820
22.3%
5 294
 
8.0%
6 117
 
3.2%
7 40
 
1.1%
8 25
 
0.7%
9 41
 
1.1%
10 9
 
0.2%
Value Count Frequency (%)
21 1
 
< 0.1%
20 3
 
0.1%
18 4
 
0.1%
17 3
 
0.1%
16 8
 
0.2%
14 2
 
0.1%
13 4
 
0.1%
12 22
0.6%
11 4
 
0.1%
10 9
0.2%

balcony
Categorical

Distinct 5
Distinct (%) 0.1%
Missing 0
Missing (%) 0.0%
Memory size 238.1 KiB
3+
1172 
3
1074 
2
884 
1
365 
0
182 

Length

Max length 2
Median length 1
Mean length 1.3187381
Min length 1

Characters and Unicode

Total characters 4849
Distinct characters 5
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 3
2nd row 2
3rd row 3
4th row 3+
5th row 3+

Common Values

Value Count Frequency (%)
3+ 1172
31.9%
3 1074
29.2%
2 884
24.0%
1 365
 
9.9%
0 182
 
4.9%

Length

2025-06-26T05:19:03.556552 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-26T05:19:03.635158 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
3 2246
61.1%
2 884
 
24.0%
1 365
 
9.9%
0 182
 
4.9%

Most occurring characters

Value Count Frequency (%)
3 2246
46.3%
+ 1172
24.2%
2 884
 
18.2%
1 365
 
7.5%
0 182
 
3.8%

Most occurring categories

Value Count Frequency (%)
Decimal Number 3677
75.8%
Math Symbol 1172
 
24.2%

Most frequent character per category

Decimal Number
Value Count Frequency (%)
3 2246
61.1%
2 884
 
24.0%
1 365
 
9.9%
0 182
 
4.9%
Math Symbol
Value Count Frequency (%)
+ 1172
100.0%

Most occurring scripts

Value Count Frequency (%)
Common 4849
100.0%

Most frequent character per script

Common
Value Count Frequency (%)
3 2246
46.3%
+ 1172
24.2%
2 884
 
18.2%
1 365
 
7.5%
0 182
 
3.8%

Most occurring blocks

Value Count Frequency (%)
ASCII 4849
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
3 2246
46.3%
+ 1172
24.2%
2 884
 
18.2%
1 365
 
7.5%
0 182
 
3.8%

floorNum
Real number (ℝ)

Zeros 

Distinct 43
Distinct (%) 1.2%
Missing 19
Missing (%) 0.5%
Infinite 0
Infinite (%) 0.0%
Mean 6.7982504
Minimum 0
Maximum 51
Zeros 129
Zeros (%) 3.5%
Negative 0
Negative (%) 0.0%
Memory size 57.5 KiB
2025-06-26T05:19:03.763543 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum 0
5-th percentile 1
Q1 2
median 5
Q3 10
95-th percentile 18
Maximum 51
Range 51
Interquartile range (IQR) 8

Descriptive statistics

Standard deviation 6.0124542
Coefficient of variation (CV) 0.884412
Kurtosis 4.5153928
Mean 6.7982504
Median Absolute Deviation (MAD) 3
Skewness 1.6936988
Sum 24868
Variance 36.149606
Monotonicity Not monotonic
2025-06-26T05:19:03.889882 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
Value Count Frequency (%)
3 498
13.5%
2 493
13.4%
1 351
 
9.5%
4 316
 
8.6%
8 195
 
5.3%
6 183
 
5.0%
10 179
 
4.9%
7 176
 
4.8%
5 169
 
4.6%
9 161
 
4.4%
Other values (33) 937
25.5%
Value Count Frequency (%)
0 129
 
3.5%
1 351
9.5%
2 493
13.4%
3 498
13.5%
4 316
8.6%
5 169
 
4.6%
6 183
 
5.0%
7 176
 
4.8%
8 195
 
5.3%
9 161
 
4.4%
Value Count Frequency (%)
51 1
 
< 0.1%
45 1
 
< 0.1%
44 1
 
< 0.1%
43 2
0.1%
40 1
 
< 0.1%
39 2
0.1%
38 1
 
< 0.1%
35 2
0.1%
34 2
0.1%
33 4
0.1%

facing
Categorical

High correlation  Missing 

Distinct 8
Distinct (%) 0.3%
Missing 1045
Missing (%) 28.4%
Memory size 258.1 KiB
East
623 
North-East
623 
North
387 
West
249 
South
231 
Other values (3)
519 

Length

Max length 10
Median length 5
Mean length 6.8381459
Min length 4

Characters and Unicode

Total characters 17998
Distinct characters 13
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row South-West
2nd row North-East
3rd row North
4th row East
5th row East

Common Values

Value Count Frequency (%)
East 623
16.9%
North-East 623
16.9%
North 387
 
10.5%
West 249
 
6.8%
South 231
 
6.3%
North-West 193
 
5.2%
South-East 173
 
4.7%
South-West 153
 
4.2%
(Missing) 1045
28.4%

Length

2025-06-26T05:19:04.014808 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-26T05:19:04.113433 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
east 623
23.7%
north-east 623
23.7%
north 387
14.7%
west 249
 
9.5%
south 231
 
8.8%
north-west 193
 
7.3%
south-east 173
 
6.6%
south-west 153
 
5.8%

Most occurring characters

Value Count Frequency (%)
t 3774
21.0%
s 2014
11.2%
o 1760
9.8%
h 1760
9.8%
E 1419
 
7.9%
a 1419
 
7.9%
N 1203
 
6.7%
r 1203
 
6.7%
- 1142
 
6.3%
W 595
 
3.3%
Other values (3) 1709
9.5%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 13082
72.7%
Uppercase Letter 3774
 
21.0%
Dash Punctuation 1142
 
6.3%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
t 3774
28.8%
s 2014
15.4%
o 1760
13.5%
h 1760
13.5%
a 1419
 
10.8%
r 1203
 
9.2%
e 595
 
4.5%
u 557
 
4.3%
Uppercase Letter
Value Count Frequency (%)
E 1419
37.6%
N 1203
31.9%
W 595
15.8%
S 557
 
14.8%
Dash Punctuation
Value Count Frequency (%)
- 1142
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 16856
93.7%
Common 1142
 
6.3%

Most frequent character per script

Latin
Value Count Frequency (%)
t 3774
22.4%
s 2014
11.9%
o 1760
10.4%
h 1760
10.4%
E 1419
 
8.4%
a 1419
 
8.4%
N 1203
 
7.1%
r 1203
 
7.1%
W 595
 
3.5%
e 595
 
3.5%
Other values (2) 1114
 
6.6%
Common
Value Count Frequency (%)
- 1142
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 17998
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
t 3774
21.0%
s 2014
11.2%
o 1760
9.8%
h 1760
9.8%
E 1419
 
7.9%
a 1419
 
7.9%
N 1203
 
6.7%
r 1203
 
6.7%
- 1142
 
6.3%
W 595
 
3.3%
Other values (3) 1709
9.5%

agePossession
Categorical

Distinct 6
Distinct (%) 0.2%
Missing 0
Missing (%) 0.0%
Memory size 280.9 KiB
Relatively New
1646 
New Property
696 
Moderately Old
563 
Undefined
306 
Old Property
303 

Length

Max length 18
Median length 14
Mean length 13.217841
Min length 9

Characters and Unicode

Total characters 48602
Distinct characters 25
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Old Property
2nd row New Property
3rd row Relatively New
4th row Relatively New
5th row Moderately Old

Common Values

Value Count Frequency (%)
Relatively New 1646
44.8%
New Property 696
18.9%
Moderately Old 563
 
15.3%
Undefined 306
 
8.3%
Old Property 303
 
8.2%
Under Construction 163
 
4.4%

Length

2025-06-26T05:19:04.235883 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-26T05:19:04.333099 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
new 2342
33.2%
relatively 1646
23.4%
property 999
14.2%
old 866
 
12.3%
moderately 563
 
8.0%
undefined 306
 
4.3%
under 163
 
2.3%
construction 163
 
2.3%

Most occurring characters

Value Count Frequency (%)
e 8534
17.6%
l 4721
 
9.7%
t 3534
 
7.3%
3371
 
6.9%
y 3208
 
6.6%
r 2887
 
5.9%
N 2342
 
4.8%
w 2342
 
4.8%
a 2209
 
4.5%
d 2204
 
4.5%
Other values (15) 13250
27.3%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 38183
78.6%
Uppercase Letter 7048
 
14.5%
Space Separator 3371
 
6.9%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 8534
22.4%
l 4721
12.4%
t 3534
9.3%
y 3208
 
8.4%
r 2887
 
7.6%
w 2342
 
6.1%
a 2209
 
5.8%
d 2204
 
5.8%
i 2115
 
5.5%
o 1888
 
4.9%
Other values (7) 4541
11.9%
Uppercase Letter
Value Count Frequency (%)
N 2342
33.2%
R 1646
23.4%
P 999
14.2%
O 866
 
12.3%
M 563
 
8.0%
U 469
 
6.7%
C 163
 
2.3%
Space Separator
Value Count Frequency (%)
3371
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 45231
93.1%
Common 3371
 
6.9%

Most frequent character per script

Latin
Value Count Frequency (%)
e 8534
18.9%
l 4721
10.4%
t 3534
 
7.8%
y 3208
 
7.1%
r 2887
 
6.4%
N 2342
 
5.2%
w 2342
 
5.2%
a 2209
 
4.9%
d 2204
 
4.9%
i 2115
 
4.7%
Other values (14) 11135
24.6%
Common
Value Count Frequency (%)
3371
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 48602
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 8534
17.6%
l 4721
 
9.7%
t 3534
 
7.3%
3371
 
6.9%
y 3208
 
6.6%
r 2887
 
5.9%
N 2342
 
4.8%
w 2342
 
4.8%
a 2209
 
4.5%
d 2204
 
4.5%
Other values (15) 13250
27.3%

super_built_up_area
Real number (ℝ)

High correlation  Missing 

Distinct 593
Distinct (%) 31.6%
Missing 1802
Missing (%) 49.0%
Infinite 0
Infinite (%) 0.0%
Mean 1925.2376
Minimum 89
Maximum 10000
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 57.5 KiB
2025-06-26T05:19:04.471318 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum 89
5-th percentile 767
Q1 1479.5
median 1828
Q3 2215
95-th percentile 3185
Maximum 10000
Range 9911
Interquartile range (IQR) 735.5

Descriptive statistics

Standard deviation 764.17218
Coefficient of variation (CV) 0.39692356
Kurtosis 10.349191
Mean 1925.2376
Median Absolute Deviation (MAD) 372
Skewness 1.8364563
Sum 3609820.5
Variance 583959.12
Monotonicity Not monotonic
2025-06-26T05:19:04.644222 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
1950 37
 
1.0%
1650 37
 
1.0%
1578 25
 
0.7%
2000 25
 
0.7%
1640 22
 
0.6%
2150 22
 
0.6%
1900 19
 
0.5%
2408 19
 
0.5%
1930 18
 
0.5%
1350 17
 
0.5%
Other values (583) 1634
44.4%
(Missing) 1802
49.0%
Value Count Frequency (%)
89 1
< 0.1%
145 1
< 0.1%
161 1
< 0.1%
215 1
< 0.1%
216 1
< 0.1%
325 1
< 0.1%
340 1
< 0.1%
352 1
< 0.1%
380 1
< 0.1%
406 1
< 0.1%
Value Count Frequency (%)
10000 1
< 0.1%
6926 1
< 0.1%
6000 1
< 0.1%
5800 2
0.1%
5514 1
< 0.1%
5350 2
0.1%
5200 2
0.1%
4890 1
< 0.1%
4857 1
< 0.1%
4848 2
0.1%

built_up_area
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct 644
Distinct (%) 38.1%
Missing 1987
Missing (%) 54.0%
Infinite 0
Infinite (%) 0.0%
Mean 2379.5858
Minimum 2
Maximum 737147
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 57.5 KiB
2025-06-26T05:19:04.780343 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum 2
5-th percentile 240.45
Q1 1100
median 1650
Q3 2400
95-th percentile 4691
Maximum 737147
Range 737145
Interquartile range (IQR) 1300

Descriptive statistics

Standard deviation 17942.88
Coefficient of variation (CV) 7.5403375
Kurtosis 1667.8704
Mean 2379.5858
Median Absolute Deviation (MAD) 650
Skewness 40.706572
Sum 4021500
Variance 3.2194695 × 108
Monotonicity Not monotonic
2025-06-26T05:19:04.926981 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
1800 41
 
1.1%
3240 37
 
1.0%
1900 34
 
0.9%
1350 33
 
0.9%
2700 33
 
0.9%
900 28
 
0.8%
1600 26
 
0.7%
1300 24
 
0.7%
2000 24
 
0.7%
1700 23
 
0.6%
Other values (634) 1387
37.7%
(Missing) 1987
54.0%
Value Count Frequency (%)
2 1
 
< 0.1%
14 1
 
< 0.1%
30 1
 
< 0.1%
33 1
 
< 0.1%
50 3
0.1%
53 1
 
< 0.1%
55 1
 
< 0.1%
56 1
 
< 0.1%
57 1
 
< 0.1%
60 5
0.1%
Value Count Frequency (%)
737147 1
 
< 0.1%
13500 1
 
< 0.1%
11286 1
 
< 0.1%
9500 1
 
< 0.1%
9000 7
0.2%
8775 1
 
< 0.1%
8286 1
 
< 0.1%
8067.8 1
 
< 0.1%
8000 1
 
< 0.1%
7500 2
 
0.1%

carpet_area
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct 733
Distinct (%) 39.2%
Missing 1805
Missing (%) 49.1%
Infinite 0
Infinite (%) 0.0%
Mean 2529.1795
Minimum 15
Maximum 607936
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 57.5 KiB
2025-06-26T05:19:05.067123 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum 15
5-th percentile 350
Q1 843
median 1300
Q3 1790
95-th percentile 2950
Maximum 607936
Range 607921
Interquartile range (IQR) 947

Descriptive statistics

Standard deviation 22799.836
Coefficient of variation (CV) 9.0147166
Kurtosis 604.53764
Mean 2529.1795
Median Absolute Deviation (MAD) 472.5
Skewness 24.333239
Sum 4734624
Variance 5.1983254 × 108
Monotonicity Not monotonic
2025-06-26T05:19:05.221042 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
1400 42
 
1.1%
1800 35
 
1.0%
1600 35
 
1.0%
1200 31
 
0.8%
1500 29
 
0.8%
1650 28
 
0.8%
1350 27
 
0.7%
1300 23
 
0.6%
1000 22
 
0.6%
1450 22
 
0.6%
Other values (723) 1578
42.9%
(Missing) 1805
49.1%
Value Count Frequency (%)
15 1
 
< 0.1%
33 1
 
< 0.1%
48 1
 
< 0.1%
50 1
 
< 0.1%
59 1
 
< 0.1%
60 1
 
< 0.1%
66 1
 
< 0.1%
72 1
 
< 0.1%
76.44 3
0.1%
77.31 1
 
< 0.1%
Value Count Frequency (%)
607936 1
< 0.1%
569243 1
< 0.1%
514396 1
< 0.1%
64529 1
< 0.1%
64412 1
< 0.1%
58141 1
< 0.1%
54917 1
< 0.1%
48811 1
< 0.1%
45966 1
< 0.1%
34401 1
< 0.1%

study room
Categorical

Distinct 2
Distinct (%) 0.1%
Missing 0
Missing (%) 0.0%
Memory size 237.0 KiB
0
2972 
1
705 

Length

Max length 1
Median length 1
Mean length 1
Min length 1

Characters and Unicode

Total characters 3677
Distinct characters 2
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0
2nd row 0
3rd row 0
4th row 1
5th row 0

Common Values

Value Count Frequency (%)
0 2972
80.8%
1 705
 
19.2%

Length

2025-06-26T05:19:05.371665 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-26T05:19:05.442409 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
0 2972
80.8%
1 705
 
19.2%

Most occurring characters

Value Count Frequency (%)
0 2972
80.8%
1 705
 
19.2%

Most occurring categories

Value Count Frequency (%)
Decimal Number 3677
100.0%

Most frequent character per category

Decimal Number
Value Count Frequency (%)
0 2972
80.8%
1 705
 
19.2%

Most occurring scripts

Value Count Frequency (%)
Common 3677
100.0%

Most frequent character per script

Common
Value Count Frequency (%)
0 2972
80.8%
1 705
 
19.2%

Most occurring blocks

Value Count Frequency (%)
ASCII 3677
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
0 2972
80.8%
1 705
 
19.2%

servant room
Categorical

High correlation 

Distinct 2
Distinct (%) 0.1%
Missing 0
Missing (%) 0.0%
Memory size 237.0 KiB
0
2349 
1
1328 

Length

Max length 1
Median length 1
Mean length 1
Min length 1

Characters and Unicode

Total characters 3677
Distinct characters 2
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0
2nd row 0
3rd row 0
4th row 1
5th row 0

Common Values

Value Count Frequency (%)
0 2349
63.9%
1 1328
36.1%

Length

2025-06-26T05:19:05.520643 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-26T05:19:05.605772 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
0 2349
63.9%
1 1328
36.1%

Most occurring characters

Value Count Frequency (%)
0 2349
63.9%
1 1328
36.1%

Most occurring categories

Value Count Frequency (%)
Decimal Number 3677
100.0%

Most frequent character per category

Decimal Number
Value Count Frequency (%)
0 2349
63.9%
1 1328
36.1%

Most occurring scripts

Value Count Frequency (%)
Common 3677
100.0%

Most frequent character per script

Common
Value Count Frequency (%)
0 2349
63.9%
1 1328
36.1%

Most occurring blocks

Value Count Frequency (%)
ASCII 3677
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
0 2349
63.9%
1 1328
36.1%

store room
Categorical

Imbalance 

Distinct 2
Distinct (%) 0.1%
Missing 0
Missing (%) 0.0%
Memory size 237.0 KiB
0
3339 
1
338 

Length

Max length 1
Median length 1
Mean length 1
Min length 1

Characters and Unicode

Total characters 3677
Distinct characters 2
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0
2nd row 0
3rd row 0
4th row 0
5th row 0

Common Values

Value Count Frequency (%)
0 3339
90.8%
1 338
 
9.2%

Length

2025-06-26T05:19:05.688281 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-26T05:19:05.755003 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
0 3339
90.8%
1 338
 
9.2%

Most occurring characters

Value Count Frequency (%)
0 3339
90.8%
1 338
 
9.2%

Most occurring categories

Value Count Frequency (%)
Decimal Number 3677
100.0%

Most frequent character per category

Decimal Number
Value Count Frequency (%)
0 3339
90.8%
1 338
 
9.2%

Most occurring scripts

Value Count Frequency (%)
Common 3677
100.0%

Most frequent character per script

Common
Value Count Frequency (%)
0 3339
90.8%
1 338
 
9.2%

Most occurring blocks

Value Count Frequency (%)
ASCII 3677
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
0 3339
90.8%
1 338
 
9.2%

pooja room
Categorical

Distinct 2
Distinct (%) 0.1%
Missing 0
Missing (%) 0.0%
Memory size 237.0 KiB
0
3021 
1
656 

Length

Max length 1
Median length 1
Mean length 1
Min length 1

Characters and Unicode

Total characters 3677
Distinct characters 2
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0
2nd row 0
3rd row 0
4th row 0
5th row 0

Common Values

Value Count Frequency (%)
0 3021
82.2%
1 656
 
17.8%

Length

2025-06-26T05:19:05.838448 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-26T05:19:05.906636 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
0 3021
82.2%
1 656
 
17.8%

Most occurring characters

Value Count Frequency (%)
0 3021
82.2%
1 656
 
17.8%

Most occurring categories

Value Count Frequency (%)
Decimal Number 3677
100.0%

Most frequent character per category

Decimal Number
Value Count Frequency (%)
0 3021
82.2%
1 656
 
17.8%

Most occurring scripts

Value Count Frequency (%)
Common 3677
100.0%

Most frequent character per script

Common
Value Count Frequency (%)
0 3021
82.2%
1 656
 
17.8%

Most occurring blocks

Value Count Frequency (%)
ASCII 3677
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
0 3021
82.2%
1 656
 
17.8%

others
Categorical

Distinct 2
Distinct (%) 0.1%
Missing 0
Missing (%) 0.0%
Memory size 237.0 KiB
0
3272 
1
405 

Length

Max length 1
Median length 1
Mean length 1
Min length 1

Characters and Unicode

Total characters 3677
Distinct characters 2
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0
2nd row 0
3rd row 1
4th row 0
5th row 0

Common Values

Value Count Frequency (%)
0 3272
89.0%
1 405
 
11.0%

Length

2025-06-26T05:19:05.986849 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-26T05:19:06.060285 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
0 3272
89.0%
1 405
 
11.0%

Most occurring characters

Value Count Frequency (%)
0 3272
89.0%
1 405
 
11.0%

Most occurring categories

Value Count Frequency (%)
Decimal Number 3677
100.0%

Most frequent character per category

Decimal Number
Value Count Frequency (%)
0 3272
89.0%
1 405
 
11.0%

Most occurring scripts

Value Count Frequency (%)
Common 3677
100.0%

Most frequent character per script

Common
Value Count Frequency (%)
0 3272
89.0%
1 405
 
11.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 3677
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
0 3272
89.0%
1 405
 
11.0%

furnishing_type
Categorical

Distinct 3
Distinct (%) 0.1%
Missing 0
Missing (%) 0.0%
Memory size 237.0 KiB
1
2404 
2
1061 
0
 
212

Length

Max length 1
Median length 1
Mean length 1
Min length 1

Characters and Unicode

Total characters 3677
Distinct characters 3
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 1
2nd row 2
3rd row 0
4th row 2
5th row 1

Common Values

Value Count Frequency (%)
1 2404
65.4%
2 1061
28.9%
0 212
 
5.8%

Length

2025-06-26T05:19:06.184024 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-26T05:19:06.339742 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
1 2404
65.4%
2 1061
28.9%
0 212
 
5.8%

Most occurring characters

Value Count Frequency (%)
1 2404
65.4%
2 1061
28.9%
0 212
 
5.8%

Most occurring categories

Value Count Frequency (%)
Decimal Number 3677
100.0%

Most frequent character per category

Decimal Number
Value Count Frequency (%)
1 2404
65.4%
2 1061
28.9%
0 212
 
5.8%

Most occurring scripts

Value Count Frequency (%)
Common 3677
100.0%

Most frequent character per script

Common
Value Count Frequency (%)
1 2404
65.4%
2 1061
28.9%
0 212
 
5.8%

Most occurring blocks

Value Count Frequency (%)
ASCII 3677
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
1 2404
65.4%
2 1061
28.9%
0 212
 
5.8%

luxury_score
Real number (ℝ)

Zeros 

Distinct 161
Distinct (%) 4.4%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 71.512918
Minimum 0
Maximum 174
Zeros 462
Zeros (%) 12.6%
Negative 0
Negative (%) 0.0%
Memory size 57.5 KiB
2025-06-26T05:19:06.710354 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum 0
5-th percentile 0
Q1 31
median 59
Q3 110
95-th percentile 174
Maximum 174
Range 174
Interquartile range (IQR) 79

Descriptive statistics

Standard deviation 53.059082
Coefficient of variation (CV) 0.74195102
Kurtosis -0.88020421
Mean 71.512918
Median Absolute Deviation (MAD) 38
Skewness 0.4590463
Sum 262953
Variance 2815.2662
Monotonicity Not monotonic
2025-06-26T05:19:06.905741 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
0 462
 
12.6%
49 348
 
9.5%
174 195
 
5.3%
44 60
 
1.6%
165 55
 
1.5%
38 55
 
1.5%
72 52
 
1.4%
60 47
 
1.3%
37 45
 
1.2%
42 45
 
1.2%
Other values (151) 2313
62.9%
Value Count Frequency (%)
0 462
12.6%
5 6
 
0.2%
6 6
 
0.2%
7 41
 
1.1%
8 30
 
0.8%
9 9
 
0.2%
12 6
 
0.2%
13 10
 
0.3%
14 12
 
0.3%
15 43
 
1.2%
Value Count Frequency (%)
174 195
5.3%
169 1
 
< 0.1%
168 9
 
0.2%
167 21
 
0.6%
166 10
 
0.3%
165 55
 
1.5%
161 3
 
0.1%
160 28
 
0.8%
159 23
 
0.6%
158 34
 
0.9%

Interactions

2025-06-26T05:18:56.973298 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:45.024677 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:46.375509 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:47.544155 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:48.610755 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:50.064701 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:51.226203 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:52.323234 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:53.436274 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:55.487079 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:57.088654 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:45.160252 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:46.479197 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:47.660383 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:48.722537 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:50.181782 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:51.352555 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:52.430968 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:53.585830 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:55.666069 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:57.237037 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:45.280658 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:46.598097 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:47.762865 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:48.837213 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:50.308262 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:51.458115 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:52.540511 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:53.761118 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:55.833967 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:57.400816 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:45.569241 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:46.718174 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:47.852368 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:48.963519 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:50.422598 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:51.552141 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:52.647271 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:53.918217 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:55.996624 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:57.671006 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:45.680229 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:46.834984 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:47.975058 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:49.093181 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:50.530630 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:51.666202 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:52.763070 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:54.103203 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:56.140799 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:57.790752 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:45.797321 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:46.968230 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:48.080032 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:49.216627 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:50.651688 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:51.772240 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:52.865616 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:54.325259 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:56.254661 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:57.899886 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:45.913333 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:47.075666 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:48.182489 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:49.568553 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:50.759403 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:51.868501 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:52.963782 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:54.482848 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:56.368538 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:58.012313 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:46.011771 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:47.194225 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:48.286363 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:49.696613 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:50.864033 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:51.967931 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:53.067726 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:54.962695 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:56.477106 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:58.138563 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:46.124441 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:47.319572 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:48.394295 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:49.819629 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:50.986055 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:52.085363 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:53.183905 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:55.134300 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:56.608284 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:58.263076 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:46.243759 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:47.430241 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:48.497330 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:49.933539 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:51.108729 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:52.192915 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:53.320437 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:55.288657 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-06-26T05:18:56.857030 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-06-26T05:19:07.079313 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Property_type agePossession area balcony bathroom bedRoom built_up_area carpet_area facing floorNum furnishing_type luxury_score others pooja room price price_per_sqft servant room store room study room super_built_up_area
Property_type 1.000 0.378 0.028 0.214 0.472 0.595 0.000 0.000 0.094 0.485 0.078 0.329 0.026 0.252 0.543 0.201 0.065 0.241 0.128 1.000
agePossession 0.378 1.000 0.000 0.272 0.105 0.126 0.000 0.000 0.090 0.128 0.208 0.256 0.108 0.185 0.100 0.056 0.279 0.143 0.140 0.082
area 0.028 0.000 1.000 0.011 0.687 0.624 0.835 0.801 0.022 0.116 0.042 0.259 0.042 0.037 0.744 0.207 0.015 0.039 0.018 0.948
balcony 0.214 0.272 0.011 1.000 0.225 0.176 0.000 0.026 0.016 0.079 0.178 0.223 0.082 0.197 0.136 0.033 0.441 0.146 0.183 0.306
bathroom 0.472 0.105 0.687 0.225 1.000 0.862 0.465 0.599 0.044 -0.005 0.200 0.179 0.070 0.286 0.720 0.411 0.520 0.244 0.176 0.819
bedRoom 0.595 0.126 0.624 0.176 0.862 1.000 0.380 0.569 0.032 -0.104 0.168 0.057 0.079 0.291 0.681 0.417 0.317 0.223 0.154 0.800
built_up_area 0.000 0.000 0.835 0.000 0.465 0.380 1.000 0.969 1.000 0.091 0.086 0.289 0.000 0.000 0.605 0.132 0.000 0.000 0.000 0.926
carpet_area 0.000 0.000 0.801 0.026 0.599 0.569 0.969 1.000 0.000 0.159 0.000 0.239 0.016 0.000 0.613 0.136 0.000 0.000 0.003 0.894
facing 0.094 0.090 0.022 0.016 0.044 0.032 1.000 0.000 1.000 0.000 0.048 0.065 0.000 0.029 0.021 0.000 0.036 0.036 0.000 0.000
floorNum 0.485 0.128 0.116 0.079 -0.005 -0.104 0.091 0.159 0.000 1.000 0.022 0.232 0.033 0.102 0.001 -0.126 0.084 0.112 0.078 0.152
furnishing_type 0.078 0.208 0.042 0.178 0.200 0.168 0.086 0.000 0.048 0.022 1.000 0.244 0.062 0.217 0.175 0.022 0.273 0.157 0.142 0.133
luxury_score 0.329 0.256 0.259 0.223 0.179 0.057 0.289 0.239 0.065 0.232 0.244 1.000 0.176 0.189 0.215 0.054 0.347 0.228 0.183 0.222
others 0.026 0.108 0.042 0.082 0.070 0.079 0.000 0.016 0.000 0.033 0.062 0.176 1.000 0.033 0.034 0.036 0.000 0.106 0.031 0.084
pooja room 0.252 0.185 0.037 0.197 0.286 0.291 0.000 0.000 0.029 0.102 0.217 0.189 0.033 1.000 0.334 0.043 0.252 0.305 0.313 0.157
price 0.543 0.100 0.744 0.136 0.720 0.681 0.605 0.613 0.021 0.001 0.175 0.215 0.034 0.334 1.000 0.744 0.369 0.303 0.244 0.772
price_per_sqft 0.201 0.056 0.207 0.033 0.411 0.417 0.132 0.136 0.000 -0.126 0.022 0.054 0.036 0.043 0.744 1.000 0.044 0.000 0.030 0.287
servant room 0.065 0.279 0.015 0.441 0.520 0.317 0.000 0.000 0.036 0.084 0.273 0.347 0.000 0.252 0.369 0.044 1.000 0.161 0.185 0.584
store room 0.241 0.143 0.039 0.146 0.244 0.223 0.000 0.000 0.036 0.112 0.157 0.228 0.106 0.305 0.303 0.000 0.161 1.000 0.226 0.046
study room 0.128 0.140 0.018 0.183 0.176 0.154 0.000 0.003 0.000 0.078 0.142 0.183 0.031 0.313 0.244 0.030 0.185 0.226 1.000 0.121
super_built_up_area 1.000 0.082 0.948 0.306 0.819 0.800 0.926 0.894 0.000 0.152 0.133 0.222 0.084 0.157 0.772 0.287 0.584 0.046 0.121 1.000

Missing values

2025-06-26T05:18:58.495924 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-26T05:18:58.711298 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-06-26T05:18:58.979129 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Property_type society sector price price_per_sqft area areaWithType bedRoom bathroom balcony floorNum facing agePossession super_built_up_area built_up_area carpet_area study room servant room store room pooja room others furnishing_type luxury_score
0 house independent sector 12 2.90 10740.0 2700.0 Plot area 2700(250.84 sq.m.) 7 7 3 2.0 South-West Old Property NaN 2700.0 NaN 0 0 0 0 0 1 7
1 flat umang winter hills sector 77 0.86 6408.0 1342.0 Super Built up area 1342(124.68 sq.m.) 2 2 2 16.0 NaN New Property 1342.0 NaN NaN 0 0 0 0 0 2 79
2 flat ambience creacions sector 22 2.55 13709.0 1860.0 Super Built up area 1860(172.8 sq.m.)Built Up area: 1600 sq.ft. (148.64 sq.m.)Carpet area: 1400 sq.ft. (130.06 sq.m.) 3 3 3 8.0 North-East Relatively New 1860.0 1600.0 1400.0 0 0 0 0 1 0 125
3 house dlf city plots phase 2 sector 25 10.00 400000.0 250.0 Plot area 250(23.23 sq.m.) 12 12 3+ 4.0 North Relatively New NaN 250.0 NaN 1 1 0 0 0 2 114
4 house suncity essel towers sector 28 8.95 17900.0 5000.0 Plot area 5000(464.52 sq.m.) 5 6 3+ 4.0 NaN Moderately Old NaN 5000.0 NaN 0 0 0 0 0 1 0
5 flat godrej summit sector 104 0.98 5950.0 1647.0 Super Built up area 1647(153.01 sq.m.) 3 3 3 12.0 East Relatively New 1647.0 NaN NaN 0 0 0 0 1 1 79
6 flat signature the roselia sector 95 0.45 7908.0 569.0 Carpet area: 569 (52.86 sq.m.) 2 2 2 2.0 East New Property NaN NaN 569.0 0 0 0 0 0 2 31
7 flat tata primanti sector 72 4.00 13769.0 2905.0 Super Built up area 2905(269.88 sq.m.)Built Up area: 2800 sq.ft. (260.13 sq.m.)Carpet area: 2500 sq.ft. (232.26 sq.m.) 4 5 3+ 9.0 North-East Relatively New 2905.0 2800.0 2500.0 0 1 0 0 0 2 165
8 house international city by sobha phase 2 sector 109 6.80 12593.0 5400.0 Plot area 600(501.68 sq.m.) 4 5 3 2.0 North-West Relatively New NaN 5400.0 NaN 0 1 0 0 0 1 49
9 flat m3m the marina sector 68 1.45 9615.0 1508.0 Super Built up area 1508(140.1 sq.m.) 3 2 3+ 9.0 NaN New Property 1508.0 NaN NaN 0 0 0 0 0 1 0
Property_type society sector price price_per_sqft area areaWithType bedRoom bathroom balcony floorNum facing agePossession super_built_up_area built_up_area carpet_area study room servant room store room pooja room others furnishing_type luxury_score
3791 flat signature global city sector 37d 0.68 11724.0 580.0 Carpet area: 580 (53.88 sq.m.) 2 2 3 9.0 NaN New Property NaN NaN 580.00 0 0 0 0 1 1 58
3792 house independent sector 49 4.45 15451.0 2880.0 Plot area 320(267.56 sq.m.) 6 6 3 3.0 North Moderately Old NaN 2880.0 NaN 1 1 1 1 0 0 152
3793 flat vatika the seven lamps sector 82 0.94 6567.0 1431.0 Super Built up area 1435(133.32 sq.m.) 2 2 2 12.0 East Relatively New 1435.0 NaN NaN 1 0 0 0 0 2 103
3794 flat tulip violet sector 69 3.10 9822.0 3156.0 Super Built up area 3156(293.2 sq.m.)Carpet area: 2500 sq.ft. (232.26 sq.m.) 4 6 3+ 14.0 North Relatively New 3156.0 NaN 2500.00 0 1 0 1 0 1 174
3796 flat smart world orchard sector 61 2.00 12911.0 1549.0 Carpet area: 1549 (143.91 sq.m.) 3 3 2 3.0 NaN New Property NaN NaN 1549.00 0 0 0 0 1 1 61
3797 flat emaar palm gardens sector 83 1.80 9473.0 1900.0 Super Built up area 1900(176.52 sq.m.)Carpet area: 1244.33 sq.ft. (115.6 sq.m.) 3 4 3 4.0 West Relatively New 1900.0 NaN 1244.33 1 1 1 1 0 2 174
3798 house not applicable sector 11 2.20 12222.0 1800.0 Plot area 1800(167.23 sq.m.) 4 3 2 1.0 South Old Property NaN 1800.0 NaN 1 0 0 1 0 1 24
3800 flat the close north sector 50 2.35 11358.0 2069.0 Super Built up area 2069(192.22 sq.m.)Built Up area: 1900 sq.ft. (176.52 sq.m.)Carpet area: 1800 sq.ft. (167.23 sq.m.) 3 3 3 8.0 North-West Moderately Old 2069.0 1900.0 1800.00 0 0 0 0 0 2 49
3801 flat umang monsoon breeze sector 78 0.90 4615.0 1950.0 Super Built up area 1950(181.16 sq.m.)Built Up area: 1900 sq.ft. (176.52 sq.m.)Carpet area: 1854 sq.ft. (172.24 sq.m.) 3 3 2 1.0 NaN Moderately Old 1950.0 1900.0 1854.00 0 1 0 1 0 1 65
3802 house dlf city plots sector 26 13.50 29880.0 4518.0 Plot area 502(419.74 sq.m.) 5 5 3 2.0 East Old Property NaN 4518.0 NaN 1 1 1 1 0 2 110